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WIDER Working Paper 2018/7 Stock-And-Flow-Consistent WIDER Working Paper 2018/7 Stock-and-flow-consistent macroeconomic model for South Africa Konstantin Makrelov,1 Channing Arndt,2 Rob Davies,3 and Laurence Harris4 January 2018 Abstract: We develop a stock-and-flow-consistent model for South Africa with four financial instruments and detailed balance sheets for the household, government, financial, non- financial, and foreign sectors and the Reserve Bank. Though micro-founded, the model departs significantly from current dynamic stochastic general equilibrium models as it assumes bounded rationality and no Ricardian equivalence. T he stock and flow consistency makes it better suited to studying balance sheet dynamics and the real sector/financial sector interaction. In the model, cyclical flow changes affect the long-term real and financial behaviour of institutions through their impact on the respective institutional assets and liabilities stocks. Keywords: stock and flow, macroeconomic, model, financial, computable general equilibrium JEL classification: C68, D53, D58, E44, E47 Acknowledgements: We are grateful for comments from Christopher Adam and Bassam Fattouh and support from the United Nations University World Institute for Development Economic Research. 1 National Treasury, Pretoria, South Africa, and School of Financial and Management Studies, SOAS University of London, London, UK, corresponding author: [email protected]; 2 International Food Policy Research Institute (IFPRI), Washington, DC, USA, and School of Financial and Management Studies, SOAS University of London, London, UK; 3 School of Financial and Management Studies, SOAS University of London, London, UK; 4 School of Financial and Management Studies, SOAS University of London, London, UK. This study has been prepared within the UNU-WIDER project on ‘Southern Africa—Towards Inclusive Economic Development (SA-TIED)’. Copyright © UNU-WIDER 2018 Information and requests: [email protected] ISSN 1798-7237 ISBN 978-92-9256-449-0 https://doi.org/10.35188/UNU-WIDER/2018/449-0 Typescript prepared by Luke Finley. The United Nations University World Institute for Development Economics Research provides economic analysis and policy advice with the aim of promoting sustainable and equitable development. The Institute began operations in 1985 in Helsinki, Finland, as the first research and training centre of the United Nations University. Today it is a unique blend of think tank, research institute, and UN agency—providing a range of services from policy advice to governments as well as freely available original research. The Institute is funded through income from an endowment fund with additional contributions to its work programme from Denmark, Finland, Sweden, and the United Kingdom. Katajanokanlaituri 6 B, 00160 Helsinki, Finland The views expressed in this paper are those of the author(s), and do not necessarily reflect the views of the Institute or the United Nations University, nor the programme/project donors. 1 Introduction This paper presents a financial-real stock-and-flow-consistent model of the South African economy. The model dynamics build on the simple computable general equilibrium (CGE) model developed by Devarajan and Go (1998) and incorporate elements of dynamic stochastic general equilibrium (DSGE) models and stock-and-flow models in the tradition of Backus et al. (1980) and Godley and Lavoie (2012). The model also incorporates elements of the theoretical models developed by Borio and Zhu (2012) and Woodford (2010). In recent decades DSGE models have been widely adopted by central banks, finance ministries, and policy analysts; however, they have been subject to extensive criticism, particularly with respect to financial sector dynamics (see Sims 2006; Caballero 2010; Blanchard 2016).1 In response to these criticisms, there have been significant efforts to incorporate financial dynamics in DSGE models, including the incorporation of financial accelerator mechanisms derived from Bernanke et al. (1999). In these models, a fall in firms’ net worth is accompanied by greater reliance on external financing. The mechanism creates a feedback loop between higher lending premiums, associated with the higher agency costs involved in external finance, and falling net worth. The approach is employed by Fernández- Villaverde (2010), Carrillo and Poilly (2010), and Kollmann et al. (2013) to study the impact of fiscal policy. A second modification introducing finance into DSGE models assumes that lenders can force borrowers to repay their loans only in the presence of some durable asset serving as collateral (Kiyotaki and Moore 1997); Ottonello (2013) and Fornaro (2015) study the impact of sudden stops in capital flows with such a model. In the models of Gertler and Karadi (2011) and Ellison and Tischbirek (2014) the ability of a representative bank to borrow from other financial institutions is limited by its balance sheet. The mechanism aims to capture how unconventional monetary policy interventions can reduce balance sheet constraints and increase lending. A different bank lending constraint is used by Gerali et al. (2010), in which the ability of banks to extend loans is limited by the holding of deposits and a capital requirements ratio imposed by the macro prudential authorities. The inclusion of such financial sector elements in DSGE models creates several problems. The models are linear and thus cannot capture the boom-and-bust dynamics that characterize the financial sector and do not capture heterogeneous and systemic risk, which are important drivers of financial sector dynamics. The inclusion of a financial accelerator mechanism increases the persistence of shocks rather than creating boom-and-bust dynamics (Borio and Zhu 2012; Duca and Muellbauer 2014). Balance sheet dynamics are either not represented at all or considered only for the balance sheet of a representative bank (Gerali et al. (2010) Gertler and Karadi 2011). But, as Calvo et al. (2004), Eggertsson and Krugman (2012), and Borio and Zhu (2012) argue, disaggregated balance sheet dynamics are important for studying 1 Two milestones in the development of DSGE models have contributed to their adoption for policy analysis. The seminal work of Smets and Wouters (2003) was the first to estimate a micro-founded DSGE model using Bayesian estimation and use it to forecast. The model consists of seven variables (GDP, consumption, investment, prices, real wages, employment, and the nominal interest rate) and ten structural shocks (including productivity, labour supply, investment preferences, cost-push, and monetary policy shocks). Christiano et al. (2005) introduced several variations designed to account for aspects of economies that policymakers face: habit formation in consumer preferences; adjustment costs in investment; variable capital utilization; and firms’ requirement for credit as working capital to finance their wage bill. They also showed that an optimisation- based model with nominal and real rigidities can account successfully for the effects of a monetary policy shock. 1 the impacts of sudden stops, fiscal policy, and general risk behaviour of agents in the economy. To address some of these criticisms, we develop a model that is stock-and-flow-consistent.2 This implies that we have several financial instruments, rates of return, and institutional balance sheets. We model equities, bonds, loans, and cash and deposits as financial instruments; their returns; and the balance sheets of the Central Bank, the household sector, the financial sector, government, the non-financial sector, and the foreign sector. This is a significantly richer representation than the financial representation of institutions and financial instruments in DSGE models. The stock and flow consistency implies that there are strict budget constraints. Changes to the balance sheet of one institution must be matched by changes to the balance sheets of other institutions. These changes reflect that some institutions save more than they invest in physical capital and thus increase their net financial assets. At the same time, those institutions that record higher investment in physical capital than their savings see an increase in their net financial liabilities. The changes to the balance sheets also reflect changes to the prices of assets and liabilities. What is a particularly striking difference between stock-and-flow-consistent models and other models is that cyclical flow changes affect the long-term real and financial behaviour of institutions through their impact on the respective institutional assets and liabilities stocks (Backus et al. 1980). Recent analyses using stock-and-flow-consistent models include Barwell and Burrows’ (2014) study of the evolution of the UK economy in the years leading up to the financial crisis of 2008, in which balance sheet linkages enable financial fragilities to be identified. In their stock-and-flow-consistent model, Caiani et al. (2014) analyse the monetary dynamics that emerge from a Schumpeterian structural change in the economy driven by innovation. Burgess et al. (2016) develop a stock-and-flow-consistent model for the United Kingdom and use it to study the impact of house price changes, shocks to the risk-weighted capital ratio, government consumption shocks, and sudden-stop shocks. They also highlight some of the problems associated with stock-and-flow-consistent models as compared with DSGE models. These problems include model equations which are not based on the optimization problem of individual
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